from pathlib import Path
import pandas as pd
from immuneML.data_model.dataset.ReceptorDataset import ReceptorDataset
from immuneML.reports.ReportOutput import ReportOutput
from immuneML.reports.ReportResult import ReportResult
from immuneML.reports.data_reports.DataReport import DataReport
from immuneML.util.PathBuilder import PathBuilder
[docs]class GLIPH2Exporter(DataReport):
"""
Report which exports the receptor data to GLIPH2 format so that it can be directly used in GLIPH2 tool. Currently, the report accepts only
receptor datasets.
GLIPH2 publication: Huang H, Wang C, Rubelt F, Scriba TJ, Davis MM. Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor
clustering with GLIPH2 and genome-wide antigen screening. Nature Biotechnology. Published online April 27,
2020:1-9. `doi:10.1038/s41587-020-0505-4 <https://www.nature.com/articles/s41587-020-0505-4>`_
Arguments:
condition (str): name of the parameter present in the receptor metadata in the dataset; condition can be anything which can be processed in
GLIPH2, such as tissue type or treatment.
YAML specification:
.. indent with spaces
.. code-block:: yaml
my_gliph2_exporter: # user-defined name
GLIPH2Exporter:
condition: epitope # for instance, epitope parameter is present in receptors' metadata with values such as "MtbLys" for Mycobacterium tuberculosis (as shown in the original paper).
"""
[docs] @classmethod
def build_object(cls, **kwargs):
return GLIPH2Exporter(**kwargs)
def __init__(self, dataset: ReceptorDataset = None, result_path: Path = None, name: str = None, condition: str = None):
super().__init__(dataset, result_path, name)
self.condition = condition
def _generate(self) -> ReportResult:
PathBuilder.build(self.result_path)
alpha_chains, beta_chains, trbv, trbj, subject_condition, count = [], [], [], [], [], []
for index, receptor in enumerate(self.dataset.get_data()):
alpha_chains.append(receptor.get_chain("alpha").amino_acid_sequence)
beta_chains.append(receptor.get_chain("beta").amino_acid_sequence)
trbv.append(receptor.get_chain("beta").metadata.v_gene)
trbj.append(receptor.get_chain("beta").metadata.j_gene)
subject_condition.append(f"{getattr(receptor.metadata, 'subject_id', str(index))}:{receptor.metadata[self.condition]}")
count.append(receptor.get_chain("beta").metadata.count
if receptor.get_chain('beta').metadata is not None and receptor.get_chain('beta').metadata.count is not None else 1)
df = pd.DataFrame({"CDR3b": beta_chains, "TRBV": trbv, "TRBJ": trbj, "CDR3a": alpha_chains, "subject:condition": subject_condition,
"count": count})
file_path = self.result_path / "exported_data.tsv"
df.to_csv(file_path, sep="\t", index=False)
return ReportResult(self.name, output_tables=[ReportOutput(file_path, "exported data in GLIPH2 format")])
[docs] def check_prerequisites(self):
if isinstance(self.dataset, ReceptorDataset):
return True
else:
return False